{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Python 2 and 3 compatibility\n",
    "from __future__ import print_function, absolute_import, division, unicode_literals, with_statement"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import torch, torchvision\n",
    "from cleanlab.models.mnist_pytorch import CNN, MNIST_TEST_SIZE, MNIST_TRAIN_SIZE\n",
    "import cleanlab\n",
    "import numpy as np\n",
    "from PIL import Image\n",
    "\n",
    "import sys\n",
    "import os\n",
    "import traceback\n",
    "from datetime import datetime as dt\n",
    "import copy\n",
    "\n",
    "from sklearn.metrics import confusion_matrix, accuracy_score\n",
    "\n",
    "# Important! Make fonts Type I fonts (necessary for publishing in ICML and other conference)\n",
    "import matplotlib\n",
    "matplotlib.rcParams['pdf.fonttype'] = 42\n",
    "matplotlib.rcParams['ps.fonttype'] = 42\n",
    "\n",
    "from torchvision import datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = np.arange(MNIST_TRAIN_SIZE)\n",
    "X_test = np.arange(MNIST_TEST_SIZE)\n",
    "# X_train = X_train[X_train % 10 == 0]\n",
    "y_train = datasets.MNIST('../data', train=True).train_labels.numpy()\n",
    "y_test = datasets.MNIST('../data', train=False).test_labels.numpy()\n",
    "py_train = cleanlab.util.value_counts(y_train) / float(len(y_train))\n",
    "X_test_data = datasets.MNIST('../data', train=False).test_data.numpy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Finding label errors in MNIST test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "red_box_idxs = [947,5937,3520, 4163, 2597,9729, 4874, 3776] # 2130, 9642,449, 659]\n",
    "\n",
    "def imshow(inp, img_labels=None, img_pred=None, img_fns = None, figsize=(10,10), normalize=False, red_boxes=True, savefig = False):\n",
    "    \"\"\"Imshow for Tensor.\"\"\"\n",
    "    height, width = inp.shape[1:]\n",
    "    xbins = 8\n",
    "    ybins = int(np.ceil(len(img_labels)/xbins))\n",
    "    xbin_width = width // xbins\n",
    "    ybin_height = height // ybins\n",
    "    \n",
    "    inp = inp.numpy().transpose((1, 2, 0))\n",
    "    if normalize:\n",
    "        mean = np.array([0.485, 0.456, 0.406])\n",
    "        std = np.array([0.229, 0.224, 0.225])\n",
    "        inp = std * inp + mean\n",
    "        inp = np.clip(inp, 0, 1)\n",
    "    \n",
    "    ax = plt.figure(figsize=figsize).gca()\n",
    "    ax.imshow(inp)\n",
    "    pad_size = (8-len(img_pred)%8)%8\n",
    "    img_labels = img_labels + ['']*pad_size #padding\n",
    "    img_pred = img_pred + ['']*pad_size #padding\n",
    "    img_fns = img_fns + ['']*pad_size #padding\n",
    "#     grid = np.asarray(img_labels).reshape((ybins, xbins))\n",
    "        \n",
    "    num_red_boxes = 0\n",
    "    for (j,i),idx in np.ndenumerate(np.arange(ybins*xbins).reshape((ybins, xbins))):\n",
    "        prediction = img_pred[idx]\n",
    "        label = img_labels[idx]\n",
    "        img_fn = img_fns[idx]\n",
    "        image_index = int(img_fn[13:])\n",
    "        \n",
    "        plt.hlines([j*ybin_height - .5], xmin=i*xbin_width, xmax=i*xbin_width + xbin_width, color = 'lightgray', linewidth=2)\n",
    "        \n",
    "        fontsize=max(min(1.4*figsize[0], .9*figsize[0]-.7*len(prediction)), 12) if prediction != '' else 1\n",
    "        tt = ax.text(i*xbin_width + xbin_width/2,j*ybin_height + ybin_height/20,prediction,ha='center',va='center', fontsize=fontsize)\n",
    "        tt.set_bbox(dict(facecolor='lime', alpha=0.8, edgecolor=None))\n",
    "        \n",
    "        fontsize=min(.5*figsize[0], 1.25*figsize[0]-len(img_fn)) if img_fn != '' else 1\n",
    "        tt = ax.text(i*xbin_width + xbin_width/2.8,j*ybin_height + ybin_height/7,img_fn,ha='center',va='center', fontsize=fontsize)\n",
    "        tt.set_bbox(dict(facecolor='lightgray', alpha=0.8, edgecolor=None))\n",
    "        \n",
    "        fontsize=max(min(1.4*figsize[0], .9*figsize[0]-.7*len(label)),12) if label != '' else 1\n",
    "        t = ax.text(i*xbin_width + xbin_width/2,j*ybin_height + ybin_height/10*9,label,ha='center',va='center', fontsize=fontsize)\n",
    "        t.set_bbox(dict(facecolor='cyan', alpha=0.8, edgecolor=None))\n",
    "        \n",
    "        if not red_boxes:\n",
    "            plt.vlines([i*xbin_width + 0.5,(i+1)*xbin_width - 1.5], ymin=j*ybin_height + 0.5, ymax=j*ybin_height + ybin_height - 0.5, color = 'gray', linewidth=5)\n",
    "        \n",
    "        if red_boxes and image_index in red_box_idxs:\n",
    "            # Draw red bounding box\n",
    "            num_red_boxes += 1\n",
    "            plt.hlines([j*ybin_height + 0.5,(j+1)*ybin_height - 1.5], xmin=i*xbin_width - 0.3, xmax=i*xbin_width + xbin_width - 0.65, color = 'red', linewidth=15)\n",
    "            plt.vlines([i*xbin_width + 0.5,(i+1)*xbin_width - 1.5], ymin=j*ybin_height + 0.5, ymax=j*ybin_height + ybin_height - 0.5, color = 'red', linewidth=15)\n",
    "            \n",
    "        \n",
    "    if red_boxes:\n",
    "        print('Number of red boxes:', num_red_boxes)\n",
    "    plt.axis('off')\n",
    "    if savefig:\n",
    "        plt.savefig('figs/mnist_test_label_errors'+str(len(img_labels))+'.pdf', pad_inches=0.0, bbox_inches='tight')\n",
    "    plt.pause(0.001)  # pause a bit so that plots are updated"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Epoch: 1 [0/10000 (0%)]\tLoss: 2.321820\n",
      "Train Epoch: 2 [0/10000 (0%)]\tLoss: 1.333377\n",
      "Train Epoch: 3 [0/10000 (0%)]\tLoss: 1.125585\n",
      "Train Epoch: 4 [0/10000 (0%)]\tLoss: 0.554050\n",
      "Train Epoch: 5 [0/10000 (0%)]\tLoss: 0.844431\n",
      "Train Epoch: 6 [0/10000 (0%)]\tLoss: 0.474832\n",
      "Train Epoch: 7 [0/10000 (0%)]\tLoss: 0.353269\n",
      "Train Epoch: 8 [0/10000 (0%)]\tLoss: 0.188346\n",
      "Train Epoch: 9 [0/10000 (0%)]\tLoss: 0.295138\n",
      "Train Epoch: 10 [0/10000 (0%)]\tLoss: 0.435071\n",
      "Train Epoch: 1 [0/7997 (0%)]\tLoss: 0.461894\n",
      "Train Epoch: 1 [0/7997 (0%)]\tLoss: 0.335446\n",
      "Train Epoch: 1 [0/8000 (0%)]\tLoss: 0.339730\n",
      "Train Epoch: 1 [0/8002 (0%)]\tLoss: 0.329118\n",
      "Train Epoch: 1 [0/8004 (0%)]\tLoss: 0.451645\n",
      "Number of estimated errors in test set: 30\n"
     ]
    },
    {
     "data": {
      "image/png": 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fnrFjx9KhQ4dyj3nqqadITU2tcJtz5syhVq1aJX9nZGSQmJjIxo0b2bdvH8eP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\nnDx5kp49e1Z4AoMEDmWKb0I9U8Da+Ky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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11604f410>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "np.random.seed(43)\n",
    "max_images = 8\n",
    "savefig = False\n",
    "prune_method = 'prune_by_noise_rate'\n",
    "\n",
    "# Pre-train\n",
    "cnn = CNN(epochs=10, log_interval=1000, loader='test') #pre-train\n",
    "cnn.fit(X_test, y_test, loader='test') # pre-train (overfit, not out-of-sample) to entire dataset.\n",
    "\n",
    "# Out-of-sample cross-validated holdout predicted probabilities\n",
    "np.random.seed(4)\n",
    "cnn.epochs = 1 # Single epoch for cross-validation (already pre-trained)\n",
    "jc, psx = cleanlab.latent_estimation.estimate_confident_joint_and_cv_pred_proba(X_test, y_test, cnn)\n",
    "est_py, est_nm, est_inv = cleanlab.latent_estimation.estimate_latent(jc, y_test)\n",
    "# algorithmic identification of label errors\n",
    "noise_idx = cleanlab.pruning.get_noise_indices(y_test, psx, est_inv, prune_method=prune_method) \n",
    "print('Number of estimated errors in test set:', sum(noise_idx))\n",
    "noise_idx = np.asarray([i in red_box_idxs for i in range(len(y_test))]) # hand-picked digits from rankpruning alg's results\n",
    "pred = np.argmax(psx, axis=1)\n",
    "\n",
    "ordered_noise_idx = np.argsort(np.asarray([psx[i][j] for i,j in enumerate(y_test)])[noise_idx])\n",
    "\n",
    "prob_given = np.asarray([psx[i][j] for i,j in enumerate(y_test)])[noise_idx][ordered_noise_idx][:max_images]\n",
    "prob_pred = np.asarray([psx[i][j] for i,j in enumerate(pred)])[noise_idx][ordered_noise_idx][:max_images]\n",
    "img_idx = np.arange(len(noise_idx))[noise_idx][ordered_noise_idx][:max_images]\n",
    "label4viz = y_test[noise_idx][ordered_noise_idx][:max_images]\n",
    "pred4viz = pred[noise_idx][ordered_noise_idx][:max_images]\n",
    "\n",
    "graphic = torchvision.utils.make_grid(torch.from_numpy(np.concatenate([X_test_data[img_idx][:, None]]*3, axis=1)))\n",
    "img_labels = [\"given: \"+str(label4viz[w])+\" | conf: \"+str(np.round(prob_given[w],3)) for w in range(len(label4viz))]\n",
    "img_pred = [\"convnet guess: \"+str(pred4viz[w])+\" | conf: \"+str(np.round(prob_pred[w],3)) for w in range(len(pred4viz))]\n",
    "img_fns = [\"train img #: \" + str(item) for item in img_idx]\n",
    "\n",
    "# Display image\n",
    "imshow(\n",
    "    graphic, \n",
    "    img_labels = img_labels, \n",
    "    img_pred = img_pred, \n",
    "    img_fns = img_fns, \n",
    "    figsize=(40,max_images/1.1), \n",
    "    red_boxes = False,  \n",
    "    savefig = savefig,\n",
    ")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Show that results generalize across epochs and seeds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of estimated errors in training set: 31\n",
      "Epochs: 8 | Random seed: 15\n",
      "Number of red boxes: 2\n"
     ]
    },
    {
     "data": {
      "image/png": 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6j+I/l1rgNtnr+ft5zx83B7AToIXFvFZjQmtJlGYTFsq3UHr1Xlk51XhNS1jG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\nzJo1ix49evDAAw8UOzVUy5YtWb9+PWlpaUFFN/v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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11605df50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epochs: 8 | Random seed: 15\n",
      "Number of red boxes: 2\n"
     ]
    },
    {
     "data": {
      "image/png": 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mKGVrlpt1ulmLjOTzlwEG9jRPzPlznsRgPmox1+5+bzCmAKhqnjTB9QKH2b6F\ncnbKKOXlibsyF8h58C4yNPsuu+d8DLnAZZ8nzyKf8WvIRapgZARrLBVP5x6FlFMWIOVLf1m2TjPF\nnL9mitZ7KlZd9R5nP1jv61/J/fU3minm/DVTQMspFamucooFmY2iP64zd4wG/g85d0HaYHcg57Hx\nXepj3ZeJkH8DRJ2H79M8MefPeVLV39DK0nYU9+0oudZtbkI60xjikRlwJgPvOz13K8pmWb8Eed+v\nI0t7e7sdrBoEZqebn5BeqQ/a3XaaiqeGNTRAjkyOyX1mt52Jn62v9RaOofV1Odt/BfyJjN6djvSO\na1XOts6OmNxm9C4Mc7rduceb8aW7n/KndopDpjcLKue1zG6rirM5XbexXmMWjuuJ2zOOxWFc13c8\ngvl64GebMb3YLmS2CEMp0oAzxIPnuARpyNmHVJjbIOdnEDK6ytAYCcYc5P22QCrV3+C4bNR25FjY\njyQBmc6wBClU+0OnG80Tc5onFdM88V6edERGnTgzevb7UwFMM8Wcv2bKMWQ/P7H+cXYZcA+On3dV\nNUQq/+4aV2KQyvAJHBug862310RnEvtMsT8fjiPTknqSc9ch35t9yDnZGhnlUxeZdtXQFpkSOgs5\nTq0om8XGPntCkNn4HkQaQxogleGxyOwU/lRD0kwx56+Z4kzLKa6qs5zijrulpcIpm5nPn2iemPPn\nPOmATEueh2OD/k7r3976DhsZku1mG/s8cZZNzZRRWiHf110m9+3Es+PTAxm5eQApp7VBOkuCY55E\nIkviPoXMctME+c7chOe54280U8z5a6Zovadi1VnvMRgdtO/Gv9pFqkIzxZy/ZgpoOaUi1VVOOYK8\nx+4uj5bOG7OQDiYhyPWeFrh2NrE+9tRuP+l0o3lizp/zBCr/G1oV2o7ivh1lL5I1znkSgswaZnZ9\nx1k3YBXSYdKsc5aPC9zimHNj+XeUrfdckTrISbESx2mwspBp6r3NeTrZI8harM62Ij3BbkN658Ug\nvU+LPHydtTj+cJwE1uC4fmF52iHhnIqc9GZ/wpEA64rrscvAO8euBBmBfB5yYcXbBiCfxwI32xiz\ntSx1un0NUpHqexb2qyLNkOO+lLKL0MY+5VK2XmJFgpCwbI8c66+RMI0z2bYRskZjFFJ4K8VxfchY\nJMSdewcbweoPHW4MmieuNE8qpnnivTwxCn5/OD3uD+vrdPNwn3yFZoorf82UxsjyRc5/OiPn/1Q8\nXzu5IsZ0vu4yxThezpmyxPp3P6pfD+Q4me2TBRjk4fOEIHnSGmmUW4AcW+fOvSAjTjpZ7zPWRb7C\nZLtI63axyJSzG3BcN9tfaKa9CGpVAAAgAElEQVS48tdMsaflFHM1UU6xZ+SpWaP0cet+DMZ1hLu/\n0Dxx5c95cilSrnb+Di9Gfht7nMFz2zsPuej9rZtteiLv1XlfdiDHpybyJARpOF6BY7vFdut+XVqJ\n52qJNKDXQS5Odcb8HKlvva8B8AsyFf8/K3jufOSc64r/zHJj0Exx5a+ZovWeitVEvedsdtD2RZop\nrvw1U0DLKRWprnJKfaRDRbLJ47YgbbbGdy8W+dydO5ZYr/eEems2keqgeeLKn/PEUJnf0KrQdhT3\n7SjG7D7OeVKErFTgSUYkIh0x/WkWcjv+NI7TcxchP87tkIb0TUhoOk/V5c4DyLRdjyIV4FPI2oSN\n8O5akxchvQ/HI9NeHUFGDDTGcS3BfGQts87IKNwQZH2++5BZAh7z4LWikPdk9H7/AhkRcJ+H+/os\nMl3Uo8g0uMZUWLuRhoIXrduNtm73b+TCxUlgGua90h5EPh9P17H8AzlGD3i4fWW1RNaP+wzZ78uQ\nQN6O/CiMQALlWqSAYkGCNg2pYHbEde3PM7GYsnU3K1qbbyyyLt544Hpkzb/3kBC37126EVl24V7K\npj0vRXrL9kHCbD8wGzk/Xnd6nfnI+dMaCeTfkalMX8CxI83NyJIxDyPr99VDAnMmMp1Yuwrej6/Q\nPDGneVIxzRPv5Um89c9/kV7OHayv/SUyVWaLCt6PL9FMMeevmRJOWWXKXjRyHMzuq6o+yHq2U5BR\nSEZj9BYkS65Cvid9kaVP8pBKbTJyfvRHMsZbpiPnwwe4f58hyHnxEjIt8RCkgWgKkn/266Cb5dRh\npBJ3HtJJZg9yfsTgOpL2c2TkeHPkfF0G/A/JJPsKdgKSxZ2Q7N1ifc5r8Wx0hy/RTDHnr5liT8sp\n5auOckoGUh65EimnlCCf3TdInsab7NdPyAjJv1Ww/75K88ScP+dJJ+Q7/D7yHe6EdPRYhSzH6K0O\nHJHId/I15D1ci3y/9iBl99HIBZ07kfdUD8mcw0ieNEaWmPUWs+9+ee5Hllx4Alk2Jx8p27RH8sWQ\nAdyI5NN4u9snI6M2GyMz2HyHDD6aiuM5/zNygaodMmX6RmCO9fXtBxC8jeTNedbnPIBk8BGkLOVP\nNFPM+WumaL3Ht+o9UNZB+3zkApiZU0g7C8iFUJAyZkOk8583P7ezTTPFnL9mCmg5xVfKKWHWx85B\nlsAcgtSZFiMzptgvEfMP4EfkfL2DsgFMnwGdob6/LHOneWLOn/Oksr+hVaXtKO7bUVognYznIOdg\nf+Q4zUXqNWPstn3eun1X63MeQToqrwWexG97r/jpblfgCSQUPkMqsz3xPFgMfZFQmoZ0HGiB/PCu\nQU5GbzHWSvzCun/NkR/xHKRQb3gVmcpzMmWfWnfk5H/Xur8V9Zi/AulJ9i4SQu2RyomnvYb7I8f0\nM6TykIcU0Dsha9sb4pF126bieOw2IZ0u7JXieQ9SkIux4ZiPVvaWMUivzLnIFIehyLG6x26bcdZt\nfkDCvL51n/6F93pNQtnxKfVg2/7I8gkfIT9a9YDLkTB1/qEvwbFHI8jyCsuRH8JGSA/FUbj+2FmQ\nH/oM5FzsjhT2nHtoXoZ872YgF8oLkHPhPqSQ6C80T8xpnnhG88Q7eQJyHnyEnN9HkfP7AaSg6080\nU8wFQqZUh5eALsiIxm+RESKdKWuIDkIqQVORTm0fIY0gI5BKrTcr/hY8Pz7XIuf9DGS/GyLnknMD\nk1lOGVMYL0QaEZohjfB34ziVPMh3ajrSOB8B9AY+RY6ZvVBktNgnyMiadkijwI0evh9fopliLhAy\nRcsp5auOcko95DOfhZyjFuQ4jAJGmrwOyDFqRs2MrvcGzRNz/p4nzyEdUmciZejWSHnCm429IB3h\nG1tf5yXkXGqJDMAxjELKAPOQBtVI5PiMxXWZgjNl9t030wH4EGlwfhK58BSPZEu43XZGucf5uB+3\nPvYIko/9kQ57ztP41wG+RxqoQcpvryLZZa8jUoZbijRiRyHTtU/AfIYtX6aZYs7fM6W6aL3Hfb0H\nPOugnQM843Tbf61/90GOm7/QTDHn75mi5RT3qquc8gjSLvIdUk8KRjrzTcTxPOiOdCz5xPq8uUhW\n3QDcDUH+crVZ88ScP+dJZX9Dz4S2o7i/3vMq8p5/RPI0Ajk+7+I4eOk86zYLkM8sCumA8yaezw7o\ng4IsFk/S3UsvFhTk/ReLxXWaprPlBPIDfQnSC8uf9EM6OjxS0ztSQw4ivfem4l+9+GubazBfO7W6\naJ54RvNE88RfaKb4h9qeKRuRhtLvqXhZE1VzajpPQDPFU7U9U7Sc4h9qOlM0TzxT2/ME5Bi8gP/O\n4FRbVDFTzqSR1qFtXjPFM7U9U7Te45Oci4vZ18BeD/PEPge8liegmeKp2p4poOUUH2NW/fQ0U7zZ\nV9KB5olnanueaDuK/6ihthSLxeJRTPlL38PqV4JMOTcA6bWVhUyXlI9jD1illKqI5olSyps0U5RS\n3qSZopTyFs0TpZQ3aaYopbxJM0Up5S2aJ0opE9rppjxByPRXbyNT24Uj03FNwXENVqWUqojmiVLK\nmzRTlFLepJmilPIWzROllDdppiilvEkzRSnlLZonSikT2ummPMGUrXEaCNbX9A4oVYtpniilvEkz\nRSnlTZopSilv0TxRSnmTZopSyps0U5RS3qJ5opQyoZ1uVO0Qh/5wKKW8Q/NEKeVNF6CZopTyHi2n\nKKW8SfNEKeUtWu9RSnmbZopSyhu0HUV5SXBN74BSSimllFJKKaWUUkoppZRSSimllFL+RjvdKKWU\nUkoppZRSSimllFJKKaWUUkopVUlBFoul+l4sKMj7L1YfCPP6syqlasJpILcGX1/zRKnAopmilPKW\nms4T0ExRKpDUdKZonigVWKqYKWfSSBtk/x/NFKX8Vhun/7c9Das9zBP7HPBanoBmilJ+yjlPwPNM\ncckBb9E8USqw1FBbisVi8SimfLLTTVRUFKGhoWd7d5SqUUVFReTn59f0btQKmimqNtBMqR6aJ6q2\n0EypHpopqrbQTKkemimqNtA88R53jbTDoqI46CZP0r2/O0rViNqeKd7qLFPR87jLFM0TFSg0T6qu\nsp1utN6jaoPanim+xNNONyFne0eqIjQ0lA8//LCmd0Ops+rBBx+s6V2oNTRTVG2gmVI9NE9UbaGZ\nUj00U1RtoZlSPTRTVG2geVI9DoaG8o2bPNlYjfui1NmkmVI93GWK5okKFJon1UfrPao20EzxP8E1\nvQNKKaWUUkoppZRSSimllFJKKaWUUkr5G+10o5RSSimllFJKKaWUUkoppZRSSimlVCVppxullFJK\nKaWUUkoppZRSSimllFJKKaUqSTvduDFv3jxuueUWkpOTz9prjB07lrFjx56151dK+Q7NFKWUt2ie\nKKW8STNFKeVNmilKKW/RPFFKeZNmilLKmzRTlFL2Qmp6BzyVlZXFww8/zKBBgxgzZkxN746yM3fu\nXBYsWMD06dOJiooiJyeHMWPGcNttt3Hdddd55TUOHDjAH3/8QVpaGmlpaRw5cgSAL7/8kjp16rh9\nbHp6OosWLSI5OZnc3FwiIyNp2bIlQ4YMYdCgQQ7blpaW8scff/Dzzz9z6NAhTp48SaNGjejSpQvX\nXnstrVu3dtj+xIkTrFy50rZfGRkZlJaWMm7cOHr27OmV967ODs0U31UdmWLIzc1l4cKFJCYmkp2d\nTWhoKE2bNqVnz57cfvvtDtu+9NJLpKSklPtcM2fOJCwszOG24uJifvzxR9asWUNGRgbBwcG0bt2a\nq666iksuucTlOSp6DYDBgwfzwAMPVOJdqrNN88R3VWeeGHJzc3nyySc5fvw4Xbp04aWXXip325SU\nFJYuXUpqair5+flERUXRunVrhg0bRu/evV223759OwsWLGDHjh0UFRXRvHlzBg8ezNChQwkOduxL\nr2UU/6WZ4rt8sd5z6tQpNmzYQGJiom37oKAg4uLiiI+PZ+jQoYSEuFb7K1tGATh+/DgLFiwgMTGR\nnJwc6tatS+fOnbnxxhvp3LmzV96/8j7NFN8VSPUegP379/PNN9+wdetWTp48SWxsLPHx8Vx//fUu\n2xvnZXkGDhzIv//970q+S3W27c/KYsjDD3PjoEGM0DzxKdWRJ+vXr+f3338nPT2d48ePc/r0aRo3\nbkyHDh0YPnw4HTt2dHlMZfMkJyeHhIQENm3axIEDBzh27BgRERG0b9+eK6+8kv79+5f7XBs3buSH\nH34gLS2N0tJSWrVqxVVXXcWll156Zm9cnTVGpmgZxff4cltKYmIiS5cu5cCBA+Tl5dGwYUPat2/P\n8OHDOeeccxy2nTJlCqtXr3b7ut27d2f8+PHl3l9UVMSzzz7L/v37adSoEVOmTKn8m1PVQus9vstX\nyynGdeP9+/eTl5dHcHAwsbGx9OzZk+HDh9O4cWOH7QsKCpg3bx67d+8mKyuL/Px86tatS5MmTbjo\noou47LLLiIiIcHhMeno6S5cuZc+ePRw5coSTJ09Sv3594uLiuOqqq+jXrx9BQUFeOQaq5vlNp5ua\ncPXVVxMfH09sbOxZe43nn3/+rD13dUlOTqZ9+/ZERUUBsGXLFkAKLN6yefNm5s+fT3BwMM2bNyc0\nNJSioqIKH7dq1SqmTZtGeHg4vXv3pkmTJhQUFLBv3z42bdrk0unmvffeY+3atTRq1Ij+/fsTERHB\nvn37WL16Nb///jvPPPMMPXr0sG2fnZ3Nl19+CUCjRo2Ijo7m+PHjXnvfKrBopnimOjIFYM+ePbz2\n2mvk5eVx3nnn0bdvX4qKisjKymLt2rUujc+Gm266yfR25wthxcXFvPbaayQnJ9OkSRNbQ8+mTZv4\n4IMP2LNnD3fccYfDYy699FK6detm+vw//fQT+fn59OrVq7JvVQUgzRPPVFee2Pv4448pLCyscLtv\nv/2WuXPnEh0dTZ8+fWjQoAF5eXmkpaWxdetWl043GzZs4K233iI0NJSBAwcSFRVFYmIiM2fOZPv2\n7Tz66KMO22sZRVWGZopnfLHes23bNiZPnkxUVBTdunWjb9++5Ofnk5iYyKxZs0hISOD55593uKBV\nlTJKdnY2EyZMICcnh44dO9KvXz/y8vJISEjgzz//5JFHHnF7IUzVLpopngmUeg/Ajh07eOWVVygu\nLmbAgAE0btyY5ORk5s+fz5YtW3j++ecJDQ11eVzbtm3p27evy+3OA55U7aV54pnqyJMNGzawe/du\nOnToQMOGDQkJCSEzM5P169fzv//9j1GjRnHZZZeZPtbTPPnxxx9ZuHAhTZs2pXv37jRo0IDs7GzW\nr19PUlISw4YNcymjGI/7/PPPiY6O5pJLLqFOnTqsW7eODz/8kPT0dEaOHHnmB0AFBM0Uz/hqW8qX\nX37JokWLiI6Opm/fvkRHR3Po0CE2bNhAQkICY8aMcRhA0K9fP5o0aWL6XGvWrCErK6vCdtbZs2dz\n+PDhyr8hVStopnjGV8spy5cvJyIigm7duhETE0NxcTFpaWksWbKEX375hRdeeIH27dvbts/Pz2fF\nihV07NiR3r17Ex0dzcmTJ9myZQszZ85k5cqVvPzyy0RGRtoes3v3bjZs2ECnTp0455xzqFu3LseP\nH2fjxo289dZbXHzxxTqTUQDRTjdu1K9fn/r165/V12jevPlZff6z7dSpU+zcuZNhw4bZbtuyZQv1\n6tVzCKMz1atXLzp37kzbtm0JCwtj7NixFRZ2duzYwbRp02jdujXPPvssDRo0cLi/uLjY4f+7du1i\n7dq1tGrVildffZXw8HDbfatWrWLq1KksWLDAodNNbGws48aNs/1geNJ7WtVemikVq65Myc/P5403\n3qC4uJiXX37ZZXS2cz7Y++c//+nRayxbtozk5GQ6d+7MuHHjbL2cT506xcSJE1myZAkXXHCBQ+Fy\n8ODBps918OBB5s+fT0xMjGmjtKp9NE8qVl15Ym/16tUkJCRwzz338Omnn5a73dq1a5k7dy49e/bk\nscceo27dug73O2dQQUEB06ZNIzg4mBdeeME2OuPmm29m4sSJrFu3jj/++IP4+HjbY7SMoipDM6Vi\nvlrvadCgAWPHjuXCCy90mNHm5MmTvPzyy6SmprJs2TKuvfZa231VKaPMmDGDnJwchg4dyp133mkb\nifX3v/+dZ599lmnTptGtWzdbI5qq3TRTKhZI9Z7S0lKmTp1KYWEhTzzxhK2+UlpayjvvvENCQgJL\nlizh+uuvd3ls27ZtPX4dVTtpnlSsuvLk3nvvNZ3lKj09nXHjxjFr1iwGDRpkOsOep9/zTp068cIL\nL7gMRjpw4ADPP/88S5Ys4eKLL6ZDhw62+7Kysvjyyy+Jiori1VdfpWnTpoB09Bk3bhyLFy9mwIAB\nLjNgqNpJM6VivtqWcuzYMX744QdiYmJ4/fXXiYmJsd2XnJzMxIkTmTdvnkunm379+rk814kTJ1i0\naBEhISFuZ8NKTk5myZIl3HPPPXzyySdn8A5VoNJMqZgvl1PeeOMN08esWLGC6dOnM2fOHJ555hnb\n7bGxsXz66aemZZ3Jkyfz22+/sXz5cofZe+Lj402v+RQUFDB+/Hh+++03hg4dSqdOnSr7lpUP8otO\nN/PmzWP+/PmA/ADbXzB44IEHGDx4sO2H9aabbqJ3797Mnz+f1NRUTpw4wXvvvUfTpk1JTk7m999/\nZ/v27eTk5FBcXEyzZs248MILue6661y+XMbrjh8/3qHB8ZZbbqFr1648+uijzJ49m8TERPLz82ne\nvDnXXnttuRdNzRg92CZPnmy7zejg8cADD9CoUSPmz59PWloaYWFh9OnThzvuuIN69eqxZ88e5s6d\nS2pqKsXFxfTo0YM777zTVrmwt2vXLmbPns2OHTsICgqiY8eO3HzzzbaRlM7v0Z3s7GxKSkoASE1N\npaSkhObNm3Po0CFAArNt27ZkZWUBEBYWRqNGjTw+Jmbi4uIq/Zgvv/yS0tJSxo4d69LhBnAJxszM\nTAB69Ojh0OEGsDUa5ebmOtweFRWlSzT4Ic0UzZQlS5aQk5PD3XffbbocglnBqbISEhIAuPHGGx2m\nFYyIiODvf/87b7zxBj/99JNHx2nFihWAdMrxxr4p79E80TwxHD58mM8//5whQ4a4HSlVWlrKV199\nRXh4OA899JBLhxtwzaB169aRm5vLoEGDHKZDDQsLY8SIEbzyyiv8/PPPDp1utIzinzRTNFMqW+9p\n164d7dq1c7m9bt26DB8+nMmTJ7N161aHTjeVLaOcPn2aTZs2ERQUxIgRIxymPm7evDmXXXYZixcv\ntjUWKd+hmaKZUh31nq1bt3LgwAG6du3qMEAgODiY22+/nYSEBH7++Weuu+46nTrdj703bx7vW/Nk\nwerVLNA8qXV5YnZRCqBNmza0bNmStLQ0cnNzz+h1yps1r2XLlgwcOJCVK1eydetWh043q1atoqio\niOuuu87huEdFRXHDDTfw0UcfsXz5cu1042PsM0XLKLUzUwyetqVkZ2djsVjo1KmTQ4cbkNky6tat\n63Ltpjxr1qzh9OnTxMfHl9thoqCggA8//JAePXpw5ZVXaqcbH6f1Hs2UqpRTynvMwIEDmT59um1/\nDcHBwQQHB5s+5sILL+S3335zeUx5rxEZGcl5553HgQMHOHTokHa6CRB+ceWuW7duFBQUsHTpUpep\nZ50bGHfs2MH3339Ply5dGDx4MHl5ebZGhIULF3LgwAHOOeccevfuTVFREdu3b7etOf3888+X+4Vx\nVlBQwIQJEwgJCWHAgAEUFRWxbt06pk6dSlBQkFfWi924cSOJiYn06dOHK664gtTUVH799VeysrK4\n7bbbeOWVVzj33HMZPHgw+/btY+PGjWRmZvL66687vI+UlBT+85//UFJSQv/+/WnWrBn79u1j4sSJ\nVZq+66WXXnIZbTl9+nSH/+fk5PDII48A0LVrVyZMmGC7z/hBOJtrKx45coRt27bRoUMHWrVqRXJy\nMrt37yYoKIi2bdvSvXt3l8/amL44OTmZ06dPO4RhYmIigMMsN8p/aaZopvz+++8EBwdzySWXsH//\nfrZs2UJhYSHNmjWjV69eLmtv2vvjjz/Izs4mJCSEuLg4evToYTpV+rFjxwBo1qyZy33GbcZUiu4U\nFxezevVqgoKCyp2qWdUczRPNEwCLxcKHH35IZGQkI0eOJD8/v9xtU1NTycrKYsCAAdSrV4/ExET2\n7dtHaGiobapRZ8nJyQCcf/75Lvd17dqV8PBwUlNTKSoqMs0j5T80UzRTvMk4H5yXbqhsGSU/P5+S\nkhJiYmJMOwraP0Y73fgWzRTNlOqo97grpzRr1owWLVqQkZFBZmamy0jdo0ePsnz5cvLy8oiOjrbN\n9KV8z4Bu3cgtKGDG0qWc27Yt3TVPal2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f5hxYQ/SaApO3fsgH/9y47xaEphgKaUjBzJT0OHekPMwcHBgn2zZ5tRPWkSu522kJMDv/89BT/9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\n3w0b3Pd3c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JNY6UDC9ASkKeWN7BThi4+mJFQHYkk0miI9iS3SExFIko6llJSURCRTSepm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\nZcooKSlJ6enpNN2UMFdrTSkJ5ykm119/vbZu3aqdO3d6mm7279+vlJQUP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      "text/plain": [
       "<matplotlib.figure.Figure at 0x107957c10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "prune_method = 'prune_by_noise_rate'\n",
    "\n",
    "# max_images = 24\n",
    "for epochs, seed in [\n",
    "    (8,15),\n",
    "    (15,14),\n",
    "    (10,14),\n",
    "    (20,13),\n",
    "    (8,13),\n",
    "    (10,12),\n",
    "    (8,12),\n",
    "    (10,10),\n",
    "    (8,9),\n",
    "    (10,3),\n",
    "    (10,2),\n",
    "    (4,14),\n",
    "    (3,13),\n",
    "    (2,12),\n",
    "]:\n",
    "        # Pre-train\n",
    "        np.random.seed(43)\n",
    "        cnn = CNN(epochs=epochs, log_interval=None, loader='test') #pre-train\n",
    "        cnn.fit(X_test, y_test, loader='test') # pre-train (overfit, not out-of-sample) to entire dataset.\n",
    "\n",
    "        # Out-of-sample cross-validated holdout predicted probabilities\n",
    "        np.random.seed(seed)\n",
    "        cnn.epochs = 1 # Single epoch for cross-validation (already pre-trained)\n",
    "        jc, psx = cleanlab.latent_estimation.estimate_confident_joint_and_cv_pred_proba(X_test, y_test, cnn)\n",
    "        est_py, est_nm, est_inv = cleanlab.latent_estimation.estimate_latent(jc, y_test)\n",
    "        # algorithmic identification of label errors\n",
    "        noise_idx = cleanlab.pruning.get_noise_indices(y_test, psx, est_inv, prune_method=prune_method)\n",
    "        print('Number of estimated errors in training set:', sum(noise_idx))\n",
    "        noise_idx = cleanlab.pruning.get_noise_indices(y_test, psx, est_inv, prune_method=prune_method, num_to_remove_per_class=5)\n",
    "        pred = np.argmax(psx, axis=1)\n",
    "\n",
    "        for max_images in [8,16]:\n",
    "            # Prepare and display figure ordered by lowest predicted probability\n",
    "            if sum(noise_idx) >= max_images:\n",
    "                print(\"Epochs:\", epochs, \"| Random seed:\", seed)\n",
    "                ordered_noise_idx = np.argsort(np.asarray([psx[i][j] for i,j in enumerate(y_test)])[noise_idx])\n",
    "\n",
    "                prob_given = np.asarray([psx[i][j] for i,j in enumerate(y_test)])[noise_idx][ordered_noise_idx][:max_images]\n",
    "                prob_pred = np.asarray([psx[i][j] for i,j in enumerate(pred)])[noise_idx][ordered_noise_idx][:max_images]\n",
    "                img_idx = np.arange(len(noise_idx))[noise_idx][ordered_noise_idx][:max_images]\n",
    "                label4viz = y_test[noise_idx][ordered_noise_idx][:max_images]\n",
    "                pred4viz = pred[noise_idx][ordered_noise_idx][:max_images]\n",
    "\n",
    "                graphic = torchvision.utils.make_grid(torch.from_numpy(np.concatenate([X_test_data[img_idx][:, None]]*3, axis=1)))\n",
    "                # graphic = np.concatenate([graphic[:, None]]*3, axis=1)\n",
    "                img_labels = [\"given: \"+str(label4viz[w])+\" | conf: \"+str(np.round(prob_given[w],3)) for w in range(len(label4viz))]\n",
    "                img_pred = [\"argmax pred: \"+str(pred4viz[w])+\" | conf: \"+str(np.round(prob_pred[w],3)) for w in range(len(pred4viz))]\n",
    "                img_fns = [\"train img #: \" + str(item) for item in img_idx]\n",
    "\n",
    "                imshow(graphic, img_labels = img_labels, img_pred = img_pred, img_fns = img_fns, figsize=(40,max_images/1.1))\n",
    "                plt.show()    "
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
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